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Using the 2017 Hurricane Harvey flood event as a test case, this study set up a series of sensitivity analyses to highlight three challenges associated with large‐scale flood inundation modeling, including (a) model parameterization, (b) errors in digital elevation models, and (c) effects of reservoir retention. Driven by radar‐based hourly rainfall data, a series of hydrologic‐hydraulic models including the VIC hydrologic model, RAPID routing model, and Flood2D‐GPU hydrodynamic model are set up over Harris County, Texas, to simulate flood inundation and hazards. The results demonstrate the importance of hydrologic parameters in improving flood modeling. For a large flood event such as Hurricane Harvey, the effect of the initial water depths is insignificant. The Manning's n values may increase the peak water depth by ~1%, the flood extents by 65km2, and the high danger zone by ~6%. On the contrary, the bathymetry correction factors may reduce the flood extent by ~1.4% and the high‐danger zone by ~4%. Reducing the reservoir storage capacity to 1% may increase the flood extent by ~4% and the high‐danger zone by ~17%. This study may provide supporting information to guide and prioritize the development of future high‐performance computing hydrodynamic large‐scale flood simulations.

This paper describes the implementation of a two-dimensional hydrodynamic flood model with two different numerical schemes on heterogeneous high-performance computing architectures. Both schemes were able to solve the nonlinear hyperbolic shallow water equations using an explicit upwind first-order approach on finite differences and finite volumes, respectively, and were conducted using MPI and CUDA. Four different test cases were simulated on the Summit supercomputer at Oak Ridge National Laboratory. Both numerical schemes scaled up to 128 nodes (768 GPUs) with a maximum 98.2x speedup of over 1 GPU. The lowest run time for the 10 day Hurricane Harvey event simulation at 5 meter resolution (272 million grid cells) was 50 minutes. GPUDirect communication proved to be more convenient than the standard communication strategy. Both strong and weak scaling are shown. CCS CONCEPTS• Computing methodologies → Massively parallel and highperformance simulations.

Context: Security bug reports are reports from bug tracking systems that include descriptions and resolutions of security vulnerabilities that occur in software projects. Researchers use security bug reports to conduct research related to software vulnerabilities. A mapping study of publications that use security bug reports can inform researchers on (i) the research topics that have been investigated, and (ii) potential research avenues in the field of software vulnerabilities. Objective: The objective of this paper is to help researchers identify research gaps related to software vulnerabilities by conducting a systematic mapping study of research publications that use security bug reports. Method: We perform a systematic mapping study of research that use security bug reports for software vulnerability research by searching five scholar databases: (i) IEEE Xplore, (ii) ACM Digital Library, (iii) ScienceDirect, (iv) Wiley Online Library, and (v) Springer Link. From the five scholar databases, we select 46 publications that use security bug reports by systematically applying inclusion and exclusion criteria. Using qualitative analysis, we identify research topics investigated in our collected set of publications. Results: We identify three research topics that are investigated in our set of 46 publications. The three topics are: (i) vulnerability classification; (ii) vulnerability report summarization; and (iii) vulnerability dataset construction. Of the studied 46 publications, 42 publications focus on vulnerability classification. Conclusion: Findings from our mapping study can be leveraged to identify research opportunities in the domains of software vulnerability classification and automated vulnerability repair techniques.